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Radiology report summarization (RRS) is a growing area of research. Given theFindings section of a radiology report, the goal is to generate a summary(called an Impression section) that highlights the key observations andconclusions of the radiology study. However, RRS currently faces essentiallimitations.First, many prior studies conduct experiments on private datasets,preventing reproduction of results and fair comparisons across differentsystems and solutions. Second, most prior approaches are evaluated solely onchest X-rays. To address these limitations, we propose a dataset (MIMIC-RRS)involving three new modalities and seven new anatomies based on the MIMIC-IIIand MIMIC-CXR datasets. We then conduct extensive experiments to evaluate theperformance of models both within and across modality-anatomy pairs inMIMIC-RRS. In addition, we evaluate their clinical efficacy via RadGraph, afactual correctness metric.

Toward expanding the scope of radiology report summarization to multiple anatomies and modalities